Cognitive Dark Matter: Measuring What AI Misses

We propose that the jagged intelligence landscape of modern AI systems arises from a missing training signal that we call ``cognitive dark matter'' (CDM): brain functions that meaningfully shape behavior yet are hard to infer from behavior alone. We identify key CDM domains---metacognition, cognitive flexibility, lifelong learning, abductive reasoning, social and common-sense reasoning, and emotional intelligence---and present evidence CDM-loaded functions are largely unmeasured in current AI benchmarks, and that large-scale neuroscience training datasets which could be used to instill these capabilities do not yet exist. We then outline a research program centered on three complementary data types designed to surface CDM for model training: (i) latent variables from large-scale cognitive models, (ii) process-tracing data such as eye-tracking and think-aloud protocols, and (iii) paired neural--behavioral data. These data will enable AI training on cognitive process rather than behavioral outcome alone, producing models with more general, less jagged intelligence. As a dual benefit, the same data will advance our understanding of human intelligence itself.